ECC transformation with hyperparameters

[rivolli]

Make this solution more generic
#14 (comment)

[parisashahnazari]

Hi,
when I run prediction function I have faced with this error:
Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class "shiny.tag"
I don't know what I should do. could you please help me to solve this error.
Regards
Parisa

My codes:

library(ggROC)
library(ROCR)
library(parallel)
library(ggplot2)
library(utiml)
#Read CSV file
data<- read.csv("D:/AMetabolomics/BC/pos/utls_pos.csv")
#Convert as data frame and indicate labels(class and size)
##Random Samples and Permutations
df<- as.data.frame(data)
df$ER<-c(sample(c(0,1), 277, replace = TRUE))
df$PR <- c(sample(c(0,1), 277, replace = TRUE))
df$HER2 <- c(sample(c(0,1), 277, replace = TRUE))
#Generates an mldr object from a data.frame and a vector with label indices
df<- mldr_from_dataframe(df, labelIndices = c(428, 429,430), name = "testMLDR")
#omit NA
df<- na.omit(df)
head(df)
#Normalize the attributes values (between 0 and 1)
df <- normalize_mldata(df)
#Split data to train and test:
ds <- create_holdout_partition(df, c(train=0.65, test=0.35), "iterative")
names(ds)
model <- br(ds$train, "RF")
predictions <- predict(model, ds$test)

[rivolli]

Hello, can you run this command: class(model) because it looks like the model variable is not what it is expected for it. Thank you. Best regards, Adriano Rívolli

…

On Tue, Apr 20, 2021 at 1:40 PM Parisa ***@***.***> wrote: Hi, when I run prediction function I have faced with this error: Error in UseMethod("predict") : no applicable method for 'predict' applied to an object of class "shiny.tag" I don't know what I should do. could you please help me to solve this error. Regards Parisa My codes: library(ggROC) library(ROCR) library(parallel) library(ggplot2) library(utiml) #Read CSV file data<- read.csv("D:/AMetabolomics/BC/pos/utls_pos.csv") #Convert as data frame and indicate labels(class and size) ##Random Samples and Permutations df<- as.data.frame(data) df$ER<-c(sample(c(0,1), 277, replace = TRUE)) df$PR <- c(sample(c(0,1), 277, replace = TRUE)) df$HER2 <- c(sample(c(0,1), 277, replace = TRUE)) #Generates an mldr object from a data.frame and a vector with label indices df<- mldr_from_dataframe(df, labelIndices = c(428, 429,430), name = "testMLDR") #omit NA df<- na.omit(df) head(df) #Normalize the attributes values (between 0 and 1) df <- normalize_mldata(df) #Split data to train and test: ds <- create_holdout_partition(df, c(train=0.65, test=0.35), "iterative") names(ds) model <- br(ds$train, "RF") predictions <- predict(model, ds$test) — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#15 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAXFKFHKI6VMRTMZEVLAVWTTJWU7PANCNFSM4RGASR6A> .